


'''simple docstring'''



def      UpperCAmelCase__   (      UpperCAmelCase_       :							str ,       UpperCAmelCase_       :							str				)	->						str:
     __lowerCamelCase  :      int														=						len(UpperCAmelCase_				)
     __lowerCamelCase  :      int														=						len(UpperCAmelCase_				)
     __lowerCamelCase  :      int														=						(
         first_str_length if first_str_length > second_str_length else second_str_length
     )
     __lowerCamelCase  :      list														=						[]
     for char_count in range(UpperCAmelCase_				):
          if char_count < first_str_length:
               output_list.append(first_str[char_count]				)
          if char_count < second_str_length:
               output_list.append(second_str[char_count]				)
     return "".join(UpperCAmelCase_				)


if __name__ == "__main__":
      print(alternative_string_arrange("""AB""", """XYZ"""), end=""" """)














"""simple docstring"""





import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin






class 	a		(   __snake_case    , unittest.TestCase						):
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					DebertaTokenizer
    SCREAMING_SNAKE_CASE		:	Optional[int]		=					True
    SCREAMING_SNAKE_CASE		:	Any		=					DebertaTokenizerFast






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Dict:
      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      lowerCamelCase_       	=		[
          'l',
          'o',
          'w',
          'e',
          'r',
          's',
          't',
          'i',
          'd',
          'n',
          '\u0120',
          '\u0120l',
          '\u0120n',
          '\u0120lo',
          '\u0120low',
          'er',
          '\u0120lowest',
          '\u0120newer',
          '\u0120wider',
          '[UNK]',
      ]
      lowerCamelCase_       	=		dict(zip(__SCREAMING_SNAKE_CASE			,		range(len(__SCREAMING_SNAKE_CASE   )   )   )   )
      lowerCamelCase_       	=		['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
      lowerCamelCase_       	=		{'unk_token': '[UNK]'}

      lowerCamelCase_       	=		os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES['vocab_file']   )
      lowerCamelCase_       	=		os.path.join(self.tmpdirname			,		VOCAB_FILES_NAMES['merges_file']   )
      with open(self.vocab_file			,		'w'			,		encoding='utf-8'   ) as fp:
        fp.write(json.dumps(__SCREAMING_SNAKE_CASE   ) + '\n'   )
      with open(self.merges_file			,		'w'			,		encoding='utf-8'   ) as fp:
        fp.write('\n'.join(__SCREAMING_SNAKE_CASE   )   )






    def   UpperCamelCase			(			self	:				Tuple			,		**__SCREAMING_SNAKE_CASE	:				Dict   )	->						List[Any]:
      kwargs.update(self.special_tokens_map   )
      return self.tokenizer_class.from_pretrained(self.tmpdirname			,		**__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				str			,		__SCREAMING_SNAKE_CASE	:				Any   )	->						Dict:
      lowerCamelCase_       	=		'lower newer'
      lowerCamelCase_       	=		'lower newer'
      return input_text, output_text






    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Optional[Any]:
      lowerCamelCase_       	=		self.get_tokenizer()
      lowerCamelCase_       	=		'lower newer'
      lowerCamelCase_       	=		['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
      lowerCamelCase_       	=		tokenizer.tokenize(__SCREAMING_SNAKE_CASE   )
      self.assertListEqual(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokens + [tokenizer.unk_token]
      lowerCamelCase_       	=		[0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE   )			,		__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Tuple:
      lowerCamelCase_       	=		self.get_tokenizer()
      lowerCamelCase_       	=		tokenizer('Hello'			,		'World'   )
      lowerCamelCase_       	=		[0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
      self.assertListEqual(tokd['token_type_ids']			,		__SCREAMING_SNAKE_CASE   )






    @slow
    def   UpperCamelCase			(			self	:				Union[str, Any]   )	->						Tuple:
      lowerCamelCase_       	=		self.tokenizer_class.from_pretrained('microsoft/deberta-base'   )

      lowerCamelCase_       	=		tokenizer.encode('sequence builders'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.encode('multi-sequence build'			,		add_special_tokens=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokenizer.encode(
          'sequence builders'			,		add_special_tokens=__SCREAMING_SNAKE_CASE			,		add_prefix_space=__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.encode(
          'sequence builders'			,		'multi-sequence build'			,		add_special_tokens=__SCREAMING_SNAKE_CASE			,		add_prefix_space=__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE   )
      lowerCamelCase_       	=		tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )

      assert encoded_sentence == encoded_text_from_decode
      assert encoded_pair == encoded_pair_from_decode






    @slow
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						Optional[Any]:
      lowerCamelCase_       	=		[self.tokenizer_class]
      if self.test_rust_tokenizer:
        tokenizer_classes.append(self.rust_tokenizer_class   )

      for tokenizer_class in tokenizer_classes:
        lowerCamelCase_       	=		tokenizer_class.from_pretrained('microsoft/deberta-base'   )

        lowerCamelCase_       	=		[
            'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
            'ALBERT incorporates two parameter reduction techniques',
            'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
            ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
            ' vocabulary embedding.',
        ]

        lowerCamelCase_       	=		tokenizer(__SCREAMING_SNAKE_CASE			,		padding=__SCREAMING_SNAKE_CASE   )
        lowerCamelCase_       	=		[tokenizer.decode(__SCREAMING_SNAKE_CASE			,		skip_special_tokens=__SCREAMING_SNAKE_CASE   ) for seq in encoding['input_ids']]

        # fmt: off
        lowerCamelCase_       	=		{
            'input_ids': [
                [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
            ],
            'token_type_ids': [
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
            ],
            'attention_mask': [
                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
                [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
            ]
        }
        # fmt: on

        lowerCamelCase_       	=		[
            'ALBERT: A Lite BERT for Self-supervised Learning of Language Representations',
            'ALBERT incorporates two parameter reduction techniques',
            'The first one is a factorized embedding parameterization. By decomposing the large vocabulary'
            ' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'
            ' vocabulary embedding.',
        ]

        self.assertDictEqual(encoding.data			,		__SCREAMING_SNAKE_CASE   )

        for expected, decoded in zip(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
          self.assertEqual(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   )




